Model Fit Information - Understanding Your Model's Performance

Model fit information provides insights into how well a machine learning model aligns with the data it was trained on. It helps assess the model's accuracy, reliability, and ability to generalize to new data. Key metrics used to evaluate model fit include:

• R-squared: Measures the proportion of variance in the dependent variable explained by the model. A higher R-squared indicates a better fit.
• Root Mean Squared Error (RMSE): Measures the average difference between predicted and actual values. A lower RMSE signifies better accuracy.
• Mean Absolute Error (MAE): Calculates the average absolute difference between predictions and actual values. A lower MAE indicates less overall error.
• Adjusted R-squared: Accounts for the number of variables in the model, providing a more robust measure of fit.

Understanding model fit information is crucial for selecting the best model for a given task. By analyzing these metrics, you can identify potential issues with overfitting or underfitting, and make informed decisions regarding model improvement and deployment.